MABAC framework for logarithmic bipolar fuzzy multiple attribute group decision-making for supplier selection

Abstract

AbstractIn this article, we introduce logarithmic operations on bipolar fuzzy numbers (BFNs). We present some new operators based on these operations, namely, the logarithm bipolar fuzzy weighted averaging (L-BFWA) operator, logarithm bipolar fuzzy ordered weighted averaging (L-BFOWA) operator, and logarithm bipolar fuzzy weighted geometric (L-BFWG) operator, and logarithm bipolar fuzzy ordered weighted geometric (L-BFOWG) operator. Further, develop a multi-attribute group decision-making (MAGDM) methodology model based on logarithm bipolar fuzzy weighted averaging operator and logarithm bipolar fuzzy weighted geometric operators. To justify the proposed model’s efficiency, MABAC (the multiple attribute border approximation area comparison) methods are applied to construct MAGDM with BFNs established on proposed operators. To demonstrate the proposed approach’s materiality and efficiency, use the proposed method to solve supply chain management by considering numerical examples for supplier selection. The selection of suppliers is investigated by aggregation operators to verify the MABAC technique. The presented method is likened to some existing accumulation operators to study the feasibility and applicability of the proposed model. We concluded that the proposed model is accurate, effective, and reliable.